About this book

This book consolidates some of the most promising advanced smart grid functionalities and provides a comprehensive set of guidelines for their implementation/evaluation using DIgSILENT Power Factory.

It includes specific aspects of modeling, simulation and analysis, for example wide-area monitoring, visualization and control, dynamic capability rating, real-time load measurement and management, interfaces and co-simulation for modeling and simulation of hybrid systems. It also presents key advanced features of modeling and automation of calculations using PowerFactory, such as the use of domain-specific (DSL) and DIgSILENT Programming (DPL) languages, and utilizes a variety of methodologies including theoretical explanations, practical examples and guidelines.

Providing a concise compilation of significant outcomes by experienced users and developers of this program, it is a valuable resource for postgraduate students and engineers working in power-system operation and planning.

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Table of Contents

Frontmatter

Future power system has several challenges. One of them is the major changes to the way of supply and use energy; building a smarter grid lies at the heart of these changes. The ability to accommodate significant volumes of decentralized and highly variable renewable generation requires that the network infrastructure must be upgraded to enable smart operation. The reliable and sophisticated solutions to the foreseen issues of the future networks are creating dynamically intelligent application/solutions to be deployed during the incremental process of building the smarter grid. The smart grid needs more powerful computing platforms (centralized and dispersed) to handle large-scale data analytic tasks and supports complicated real-time applications. The implementation of highly realistic real-time, massive, online, multi-time frame simulations is required. The objective of this chapter is to present a general introduction to the DIgSILENT PowerFactory, the most important aspects of advanced smart grid functionalities, including special aspects of modelling as well as simulation and analysis, e.g. wide area monitoring, visualization, and control; dynamic capability rating, real-time load measurement and management, interfaces and co-simulation for modelling and simulation of hybrid systems. The chapter presents a very well-documented smart grid functionality, and limited cases are explained: Virtual Control Commissioning: connection to SCADA system via OPC protocol, direct connection to Modbus TCP devices, GIS integration with PowerFactory using API. The explained cases allow showing the full potential of PowerFactory connectivity to fulfil the growing requirements of the smart grids planning and operation.

The need to set up and simulate different scenarios, and later analyse the results, is widespread in the power systems community. However, scenario management and result analysis can quickly increase in complexity as the number of scenarios grows. This complexity is particularly high when dealing with modern smart grids. The Python API provided with DIgSILENT PowerFactory is a great asset when it comes to automating simulation-related tasks. Additionally, in combination with the well-established Python libraries for data analysis, analysis of results can be greatly simplified. This chapter illustrates the synergic relationship that can be established between DIgSILENT PowerFactory and a set of Python libraries for data analysis by means of the Python API, and the simplicity with which this relationship can be established. The examples presented here show that it can be beneficial to exploit the Python API to combine DIgSILENT PowerFactory with other Python libraries and serve as evidence that the possible applications are mainly limited by the creativity of the user.

In this chapter, a user-defined tool to minimise the voltage unbalance caused by unsymmetrical loads and generators is presented. It provides the user with a set of switching actions to improve the power distribution over the three phases. The tool uses the phase connection information provided for example by the automated meter infrastructure and uses a Monte-Carlo-based search to identify the lowest voltage unbalance reachable for a given number of phase-switching actions. By doing this, the Pareto principle can be used, and the user can decide on the necessary number of switching actions and therefore on the efforts needed and justified to improve the situation. The tool, which is implemented via Python scripts, is easily accessible to the user via the user-defined button.

Smart grid concept is gaining more and more importance in electric power systems. In near term, electric grids will be more intelligent, interconnected and decentralised. Dealing with a significant number of distributed resources in a smart way frequently requires the use of optimal control techniques, which find the best solution according to a defined objective function. Taking into account all these aspects, the simulation of these types of problems is characterised by having a great number of controlled resources and the use of advanced control techniques. In this context, DIgSILENT PowerFactory provides useful tools to simulate complex systems. On the one hand, the DIgSILENT Programming Language (DPL) can be used for multiple purposes such as automation of simulations, automatic generation of simulation scenarios, analysis of results. On the other hand, the DIgSILENT Simulation Language (DSL) and the digexfun interface allow the implementation of advanced control techniques. Using the digexfun interface, DIgSILENT PowerFactory can send and receive data from other mathematical software APIs such as MATLAB. This chapter presents a co-simulation framework developed to test optimal control methods for root mean square (RMS) simulations on DIgSILENT PowerFactory. As an example, the implementation of a smart charging control for plug-in electric vehicles in electric distribution networks is explained.

Load-flow analysis is an effective tool that is commonly used to capture the power system operational performance and its state at a certain point in time. Power grid operators use load-flow extensively on a daily basis to plan for day-ahead and dispatch scheduling among many other purposes. Also, it used to plan any grid expansion, alter or modernization. However, due to the deterministic nature and its applicability for only one set of operational data at a certain period, deterministic load-flow reduces the chances for predicting the uncertainty in power system. Researchers usually create a data model using probabilistic analyses techniques to produce a stochastic model that mimics the realistic system data. Combining this model with Monte Carlo methodology leads to form a probabilistic load-flow tool that is more powerful and potent to carry on many uncertainty tasks and other aspects of power system assessment. This chapter presents the DIgSILENT PowerFactory script language (DPL) implementation of a DPL script to perform probabilistic power flow (PLF) using Monte Carlo simulations (MCS) to consider the variability of the stochastic variables in the power system during the assessment of the steady-state performance. The developed PLF script takes input data from an external Microsoft Excel file, and then, the DPL can carry on a probabilistic load-flow and export the results using a Microsoft Excel file. The suitability of the implemented DPL is illustrated using the classical IEEE 14 buses.

Power management system (PMS) which is a kind of special protection system is used to detect predefined conditions and execute timely remedial actions to prevent instability or improve operating point, especially in an islanded system. To this purpose, PMS usually includes several functions such as load shedding/sharing, generation shedding, and generation mode control which are automatically executed to improve operating point. In this chapter, to show the capability of DIgSILENT power factory to simulate smart grids functionalities, DIgSILENT programming language (DPL) is used to model PMS logic in automatically detecting islanding condition as well as executing load/generation shedding in an islanded system to prevent instability. Indeed, this modelling gives the possibility to check the impact of considered PMS logic under different operating conditions on the stability of the system.

This chapter introduces the concept of eigenvalue sensitivity to analyse the installation location and feedback signals of damping regulating devices using DIgSILENT Programming Language. A state-space representation of the linearized system is estimated by dynamic simulations and used to provide two indices based on mode controllability and mode observability. By inspection of these indices, the number of location/signal candidates is reduced, decreasing the computational cost of modal analysis and time-domain simulation for the final selection of regulating device location and feedback signals. This approach is applied to determine the location/signal of a Battery Energy Storage (BES) regulating the inter-area oscillation between the Northern Chile Interconnected System (NCIS) and the Argentinian Interconnected System (AIS).

During the last decades, insufficient investment along with an increase in power demand have caused power system operating points to get closer to their stability boundary. In this condition, the need to fast and precise assessment of system stability status and proper execution of remedial actions results in a tendency of dispatching centres towards special protection system (SPS). This system is a smart monitoring and control system which detects the predefined condition and automatically executes pre-specified remedial actions to improve system stability. Indeed, such system can reduce human faults and prevent economic loss. Power management system (PMS) is a kind of SPS which is usually implemented in industrial power plants to intelligently manage remote devices and perform required actions against system contingency, especially in islanding condition. PMS has significant functions such as load shedding/sharing, generation shedding, generation mode control and import/export control to the control voltage, frequency and active/reactive power of the system. In this chapter, PMS configuration and its major functions are explained, and its impact on system stability is analysed through detailed dynamic simulations in DIgSILENT PowerFactory software. In these simulations, DIgSILENT simulation language (DSL) is used to model turbine-governor, excitation system and signals.

This chapter presents the design of a wide-area damping control (WADC) using a power system stabiliser (PSS) and remote PMU data from the wide-area measurement system (WAMS). The WAMS and monitoring architectures are described, and the common linear design techniques for proposing the WADC are introduced. An offline heuristic optimisation method is used to tune the WADC-PSS control parameters based on modal analysis. Generators’ speed deviation is set as non-local inputs to PSS of the generators for WADC. PMU-based WADC improves damping of both the inter-area and the local oscillatory modes. A four-machine two-area power system is selected for numerical simulation using DIgSILENT PowerFactory. Modal analysis and time-domain simulation confirm the improved performance of the proposed WADC.

Over the last decade, there has been a considerable increase in deploying phasor measurement units (PMUs) in wide-area monitoring, protection, and control of power systems, as well as the development of smart transmission and distribution grid applications. This chapter is focused on the demonstration of capabilities of DIgSILENT PowerFactory software for solving the problem of optimal PMU placement in power networks. The optimal placement has been viewed from the perspective of satisfying the observability requirement of power system state estimator. Optimal placement of PMU is formulated as a practical design task, considering some technical challenges like complete network observability, enough redundancy, and the concept of zero injection buses under PMU and tie-line critical contingencies. Furthermore, the meta-heuristic techniques on the basis of evolutionary computations are programmed as an optimisation toolbox in DIgSILENT Programming Language (DPL). A distinctive characteristic of the presented module is that the evolutionary algorithm is only coded in DPL without using the time-consuming process of interlinking DIgSILENT PowerFactory with another software package like MATLAB. In summary, the bus adjacency relationship matrix, zero injection bus (ZIB), observability in the presence of ZIB, and PMU/line contingencies are programmed in different DPLs and combined together with the DPL of an evolutionary algorithm to create the optimal PMU placement module. Also, the proposed toolbox is not case-dependent and can be run with the user-defined test systems, what is contributing to the proposed tool flexibility. Finally, the applicability and efficiency of the proposed optimal PMU placement module are investigated on the DIgSILENT PowerFactory version of IEEE 14- and 39-bus test systems.

Intentional controlled islanding is a novel emergency control technique to mitigate wide-area instabilities by intelligently separating the power network into a set of self-sustainable islands. During the last decades, it has gained an increased attention due to the recent severe blackouts all over the world. Moreover, the increasing uncertainties in power system operation and planning put more requirements on the performance of the emergency control and stimulate the development of advanced System Integrity Protection Schemes (SIPS). As compared to the traditional SIPS, such as out-of-step protection, ICI is an adaptive online emergency control algorithm that aims to consider multiple objectives when separating the network. This chapter illustrates a basic ICI algorithm implemented in PowerFactory. It utilises the slow coherency theory and constrained graph partitioning in order to promote transient stability and create islands with a reasonable power balance. The algorithm is also capable to exclude specified network branches from the search space. The implementation is based on the coupling of Python and MATLAB program codes. It relies on the PowerFactory support of the Python scripting language (introduced in version 15.1) and the MATLAB Engine for Python (introduced in release 8.4). The chapter also provides a case study to illustrate the application of the presented ICI algorithm for wide-area instability mitigation in the PST 16 benchmark system.

DIgSILENT PowerFactory is an extremely powerful power system analysis software used for simulating the electrical power networks. The smart grid envisions a modernised electrical grid that uses communication to gather and act on information. The ever-increasing communication and controls in power networks increase the complexity of the system. Co-simulation becomes essential to couple system simulators from different domains. This chapter presents the Peer-to-peer MATLAB–PowerFactory communication. The method is extremely simple file sharing approach to couple MATLAB and PowerFactory, and it is used to solve an optimisation problem. An illustrative two area power system is modelled using PowerFactory and an optimisation algorithm is implemented in MATLAB. The optimisation process is used for the optimal tuning and placement of Power System Stabilizer (PSS) in order to enhance the power system stability. The optimisation algorithm used in this chapter is an evolutionary algorithm (Particle Swarm Optimisation—PSO). MATLAB and DIgSILENT are employed and linked together in a genuine automatic data exchange procedure. Consequently, the test system and the controllers are modelled in DIgSILENT and the PSO algorithm is implemented in MATLAB. For evaluating the particles evolution throughout the searching process, an eigenvalue-based multi-objective function is used. The performance of the proposed PSO-based PSS test system in damping power system oscillations is proved through eigenvalue analysis and time-domain simulations.

The development of novel smart transmission grid applications has recently gained deep interest based on the fact that there has been a wide deployment of technology capable of controlling the system in real time. In fact, some smart grid applications have been designed in order to perform timely Self-Healing and adaptive reconfiguration actions based on system-wide analysis, with the objective of reducing the risk of power system blackouts. In this new framework, real-time vulnerability assessment has to be firstly done in order to decide and coordinate the appropriate preventive or corrective control actions, such as special protection schemes. Since transient stability is, indeed, one of the most critical causes of system vulnerability, developing and applying assessment methodologies capable of delivering quick responses is fundamental among this smart structure. In this connection, a hybrid method, named Single Machine Equivalent (SIME), seems to present good perspectives of application for orienting both preventive control actions (off-line simulations) or corrective control actions (real-time analysis: emergency SIME). This method uses a combination of time-domain signals together with the equal area criterion (EAC) in order to determine the transient stability status based on the computation of stability margins. One of the challenges regarding the application of the SIME method is to adequately integrate it with a time-domain solver routine (such as the one already implemented in DIgSILENT PowerFactory). In this connection, this chapter addresses key aspects concerning the implementation of SIME by using DIgSILENT Programming Language (DPL). An application example on a well-known benchmark power system is then presented and discussed in order to highlight the feasibility and effectiveness of this implementation in DIgSILENT PowerFactory environment.

In this chapter, to cope with new challenges arising from the increasing level of power injected into the network through converter interfaces, a new wind turbine (WT) as well as a VSC–HVDC control concept, which determines the converter reference voltage directly without the need for an underlying current controller, is presented and discussed. Additionally, alternative options for frequency support by the HVDC terminals that can be incorporated into the active power control channel are presented. The implementation steps performed by using DSL programming are presented for the case of EMT simulations. Simulation results show that the control approach fulfills all the operational control functions in steady state and in contingency situations supporting fault ride through and emergency frequency support, without encountering the problems arising from current injection control.